A Bipartite Graph Approach to U.S.-China Cross-Market Return Forecasting
Authors: Jing Liu, Maria Grith, Xiaowen Dong, Mihai Cucuringu
Organizations: Department of Statistics, University of Oxford, UK · Finance Department, Neoma Business School, France · Department of Engineering Science, University of Oxford, UK · Department of Mathematics, University of California Los Angeles, US · Oxford-Man Institute of Quantitative Finance, University of Oxford, UK
This paper studies cross-market return predictability through a machine learning framework that preserves economic structure. Exploiting the non-overlapping trading hours of the U.S. and Chinese equity markets, we construct a directed bipartite graph that captures time-ordered predictive linkages between stocks across markets. Edges are selected via rolling-window hypothesis testing, and the resulting graph serves as a sparse, economically interpretable feature-selection layer for downstream machine learning models. We apply a range of regularized and ensemble methods to forecast open-to-close returns using lagged foreign-market information. Our results reveal a pronounced directional asymmetry: U.S. previous-close-to-close returns contain substantial predictive information for Chinese intraday returns, whereas the reverse effect is limited. This informational asymmetry translates into economically meaningful performance differences and highlights how structured machine learning frameworks can uncover cross-market dependencies while maintaining interpretability.
Figures & tables
Figure 1: Schematic illustration of the directed bipartite graph linking source-market stocks to target-market stocks based on significant predictive relationships.
Figure 2: Example time series of U.S. pvCLCL returns for CDNS and Chinese OPCL returns for 002410.XSHE over the rolling training window. The series are shown for illustrative purposes to highlight cross-market co-movement underlying the detected predictive link.
Figure 3: Heatmap of the directed biadjacency matrix for a representative trading day. Rows correspond to Chinese stocks and columns to U.S. stocks, grouped by sector. Each entry represents the t-statistic from the rolling-window regression of Chinese returns on lagged U.S. returns. Colour intensity reflects the magnitude and sign of the predictive relationship.
Figure 4: Sector-level heatmap of the absolute median t-statistic in the time-averaged biadjacency matrix of the directed cross-market graph. Rows correspond to Chinese sectors and columns to U.S. sectors. Each entry reports the median absolute predictive strength across all stock pairs within the corresponding sector-by-sector block.
Figure 5: The figure shows the 25th, 50th, and 75th percentiles of the in-degree distribution of target nodes by day. US-CN represents the number of U.S. pvCLCL nodes selected to predict Chinese OPCL returns, while CN-US represents the number of Chinese pvCLCL nodes selected to predict U.S. OPCL returns.
Figure 6: Schematic illustration of the rolling training and prediction framework. For each target stock Yi , returns over the look-back window [t−w,t−1] are regressed on lagged source-market returns over [t−l−w,t−l−1] . The bottom row represents the out-of-sample prediction of rYi(t) using source returns observed at t−l , thereby preserving temporal ordering and eliminating look-ahead bias.
Figure 7: Timeline of opening and closing times for the U.S. and Chinese stock markets. The non-overlapping trading sessions induce a natural temporal ordering of information, with U.S. day t−1 close preceding Chinese day t trading, and Chinese day t close preceding U.S. day t trading.
Figure 8: Sharpe Ratios for forecasting Chinese OPCL returns using U.S. pvCLCL and OPCL returns as predictors.
Figure 9: Sharpe Ratios for forecasting U.S. OPCL returns using Chinese returns as predictors.
Figure 10: Cumulative daily Profit and Loss (PnL) from forecasting Chinese OPCL returns using U.S. pvCLCL returns as predictors. Each panel corresponds to a different machine learning model, and coloured curves represent nested quantile portfolios ranked by the absolute value of predicted returns.
Figure 11: Sharpe Ratios for forecasting Chinese OPCL returns using Chinese returns as predictors.
Figure 12: Comparison of Sharpe Ratios across graph-based cross-market, graph-based same-market, and non-graph-based baseline specifications, using pvCLCL returns as predictors. CN-CN denotes using Chinese stocks to forecast Chinese stocks, while US-CN denotes using U.S. stocks to forecast Chinese stocks.
Figure 13: Performance differentials (Sharpe Ratio deltas) relative to the non-graph-based baseline, using pvCLCL returns as predictors. US-CN denotes using U.S. stocks to forecast Chinese stocks, while CN-CN denotes using Chinese stocks to forecast Chinese stocks.
Figure 14: Median forecasting performance under graph randomization (a) and increasing temporal lag (b). Panel (a) reports Sharpe Ratios as a function of the fraction of replaced edges while preserving in-degree. Panel (b) reports Sharpe Ratios as the lag parameter l increases, measuring the effect of input recency.
Appendix figures & tables5 assets
Supplementary material from the paper’s appendix.
Appendix
Figure B.1 : Comparison of Sharpe Ratios across graph-based cross-market, graph-based same-market, and non-graph-based baseline specifications, using OPCL returns as predictors. US-CN denotes using U.S. stocks to forecast Chinese stocks, while CN-CN denotes using Chinese stocks to forecast Chinese stocks.
Figure B.2 : Performance differentials (Sharpe Ratio deltas) relative to the non-graph-based baseline, using OPCL returns as predictors. US-CN denotes using U.S. stocks to forecast Chinese stocks, while CN-CN denotes using Chinese stocks to forecast Chinese stocks.
Figure C.1 : Sharpe Ratios of forecasting models conditional on SPY absolute pvCLCL return nested quantiles, measuring predictive performance following different magnitudes of U.S. market shocks.
Figure D.1 : Sector-level Sharpe Ratios for cross-market forecasting (using U.S. pvCLCL returns to forecast Chinese OPCL returns).
Figure E.1 : Sharpe Ratios for forecasting Chinese OPCL returns when the predictor set includes both U.S. and Chinese stocks. Panels vary the combination of pvCLCL and OPCL features across the two markets.
Financial forecasting uses market, company, and economic data to predict prices, returns, volatility, and business outcomes for decisions and risk management.
†Centre for Artificial Intelligence Research, University of Agder, Norway · ‡Head of AI, Bernoly AG, Zurich, Switzerland · ¶La Jolla Private Wealth Group, Wells Fargo Advisors, La Jolla, CA, USA
Goizueta Business School Emory University Atlanta, GA, USA · College of Computing Georgia Institute of Technology Atlanta, GA, USA · School of Data Science University of Pennsylvania Philadelphia, PA, USA +1